Cooperative Path Planning for Persistent Surveillance in Large-Scale Environment with UAV-UGV System
Jiahui Wang
Non-member
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116 People's Republic of China
Search for more papers by this authorKai Yang
Non-member
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116 People's Republic of China
Search for more papers by this authorBaolei Wu
Non-member
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116 People's Republic of China
Search for more papers by this authorCorresponding Author
Jun Wang
Non-member
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116 People's Republic of China
Correspondence to: Jun Wang. E-mail: [email protected]
Search for more papers by this authorJiahui Wang
Non-member
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116 People's Republic of China
Search for more papers by this authorKai Yang
Non-member
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116 People's Republic of China
Search for more papers by this authorBaolei Wu
Non-member
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116 People's Republic of China
Search for more papers by this authorCorresponding Author
Jun Wang
Non-member
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116 People's Republic of China
Correspondence to: Jun Wang. E-mail: [email protected]
Search for more papers by this authorAbstract
In this paper, we consider the path planning problem of the Unmanned Air-Ground Vehicle (UAV-UGV) system for large-scale environmental persistent surveillance. The goal is to acquire a set of periodically visited surveillance nodes while minimizing the traveling distance. Some expected key problems are the limitations of UAV speed, UGV speed, UAV endurance, and UAV field of view. To address this issue, the path planning of UAV-UGV surveillance system is modeled as a TSP optimization problem based on multiple constraints, aiming to minimize the total path length of the system to perform the task. UAV is responsible for visiting the surveillance node and UGV serves as a mobile charging station. And a two-layer chaotic aptenodytes forsteri optimization algorithm (Two-CAFO) is proposed to solve this problem. Our solution has been tested in several simulated and real-world environments. The results demonstrate that the proposed Two-CAFO has superior performance compared to other state-of-the-art algorithms in solving the path planning problem for large-scale environmental persistent surveillance tasks of UAV and UGV. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
References
- 1Bouguettaya A, Zarzour H, Kechida A, Taberkit AM. Deep learning techniques to classify agricultural crops through UAV imagery: A review. Neural Computing and Applications 2022; 34(12): 9511–9536.
- 2Zhao S, Kang F, Li J, Ma C. Structural health monitoring and inspection of dams based on UAV photogrammetry with image 3D reconstruction. Automation in Construction 2021; 130:103832.
- 3Macleod CN, Dobie G, Pierce SG, Summan R, Morozov M. Machining-based coverage path planning for automated structural inspection. IEEE Transactions on Automation Science and Engineering 2016; 15(1): 202–213.
- 4Li T, Ding F, Yang W. UAV object tracking by background cues and aberrances response suppression mechanism. Neural Computing and Applications 2021; 33: 3347–3361.
- 5Arai T, Kageyama Y, Ishizawa C, Shirai H, Ishii M, Suehiro K, Chiba T. Time series analysis of separation for vegetation management around power lines using UAV photogrammetry. IEEJ Transactions on Electrical and Electronic Engineering 2020; 15(12): 1801–1810.
- 6Torres M, Pelta DA, Verdegay JL, Torres JC. Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction. Expert Systems with Applications 2016; 55: 441–451.
- 7Saeed RA, Omri M, Abdel-Khalek S, Ali ES, Alotaibi MF. Optimal path planning for drones based on swarm intelligence algorithm. Neural Computing and Applications 2022; 34(12): 10133–10155.
- 8Hari SKK, Rathinam S, Darbha S, Kalyanam K, Manyam G, Casbeer D. Optimal UAV route planning for persistent monitoring missions. IEEE Transactions on Robotics 2020; 37(2): 550–566.
- 9Smith SL, Schwager M, Rus D. Persistent robotic tasks: Monitoring and sweeping in changing environments. IEEE Transactions on Robotics 2011; 28(2): 410–426.
- 10Nonami K. Present state and future prospect of autonomous control technology for industrial drones. IEEJ Transactions on Electrical and Electronic Engineering 2020; 15(1): 6–11.
- 11Alamdari S, Fata E, Smith SL. Persistent monitoring in discrete environments: Minimizing the maximum weighted latency between observations. The International Journal of Robotics Research 2014; 33(1): 138–154.
- 12Scherer J, Rinner B. Multi-UAV surveillance with minimum information idleness and latency constraints. IEEE Robotics and Automation Letters 2020; 5(3): 4812–4819.
- 13Chen Y, Shu Y, Hu M, Zhao X. Multi-UAV cooperative path planning with monitoring privacy preservation. Applied Sciences 2022; 12(23):12111.
- 14Jensen-Nau KR, Hermans T, Leang KK. Near-optimal area-coverage path planning of energy-constrained aerial robots with application in autonomous environmental monitoring. IEEE Transactions on Automation Science and Engineering 2020; 18(3): 1453–1468.
- 15Nigam N, Bieniawski S, Kroo I, Vian J. Control of multiple UAVs for persistent surveillance: Algorithm and flight test results. IEEE Transactions on Control Systems Technology 2011; 20(5): 1236–1251.
- 16Scherer J, Rinner B. Short and full horizon motion planning for persistent multi-UAV surveillance with energy and communication constraints. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017; 2017: 230–235.
- 17Tokekar P, Vander Hook J, Mulla D, Isler V. Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Transactions on Robotics 2016; 32(6): 1498–1511.
- 18Wang T, Huang P, Dong G. Modeling and path planning for persistent surveillance by unmanned ground vehicle. IEEE Transactions on Automation Science and Engineering 2020; 18(4): 1615–1625.
- 19Tiwari K, Xiao X, Malik A, Chong NY. A unified framework for operational range estimation of mobile robots operating on a single discharge to avoid complete immobilization. Mechatronics 2019; 57: 173–187.
- 20Chen Y, Chen M, Chen Z, Cheng L, Yang Y, Li H. Delivery path planning of heterogeneous robot system under road network constraints. Computers and Electrical Engineering 2021; 92:107197.
- 21Qin H, Meng Z, Meng W, Chen X, Sun H, Lin F, Ang MH. Autonomous exploration and mapping system using heterogeneous UAVs and UGVs in GPS-denied environments. IEEE Transactions on Vehicular Technology 2019; 68(2): 1339–1350.
- 22Ropero F, Munoz P, R-Moreno MD. TERRA: A path planning algorithm for cooperative UGVCUAV exploration. Engineering Applications of Artificial Intelligence 2019; 78: 260–272.
- 23Mathew N, Smith SL, Waslander SL. Multirobot rendezvous planning for recharging in persistent tasks. IEEE Transactions on Robotics 2015; 31(1): 128–142.
- 24Yu K, O Kane JM, Tokekar P. Coverage of an environment using energy-constrained unmanned aerial vehicles. 2019 international conference on robotics and automation (ICRA) 2019: 3259–3265.
10.1109/ICRA.2019.8794150 Google Scholar
- 25Li J, Deng G, Luo C, Lin Q, Yan Q, Ming Z. A hybrid path planning method in unmanned air/ground vehicle (UAV/UGV) cooperative systems. IEEE Transactions on Robotics 2016; 65(12): 9585–9596.
- 26Li J, Sun T, Huang X, Ma L, Lin Q, Chen J, Leung VCM. A memetic path planning algorithm for unmanned air/ground vehicle cooperative detection systems. IEEE Transactions on Automation Science and Engineering 2021; 19(4): 2724–2737.
- 27Janos J, Vonasek V, Penicka R. Multi-goal path planning using multiple random trees. IEEE Robotics and Automation Letters 2021; 6(2): 4201–4208.
- 28Cui Y, Ren J, Zhang Y. Path planning algorithm for unmanned surface vehicle based on optimized ant colony algorithm. IEEJ Transactions on Electrical and Electronic Engineering 2022; 17(7): 1027–1037.
- 29Alipour MM, Razavi SN, Feizi Derakhshi MR, Balafar MA. A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem. Neural Computing and Applications 2018; 30: 2935–2951.
- 30Qu X, Zhang R, Liu B, Li H. An improved TLBO based memetic algorithm for aerodynamic shape optimization. Engineering Applications of Artificial Intelligence 2017; 57: 1–15.
- 31Yang Z, Deng LB, Wang Y, Liu J. Aptenodytes forsteri optimization: Algorithm and applications. Knowledge-Based Systems 2021; 232:107483.
- 32Aurenhammer F. Voronoi diagramsła survey of a fundamental geometric data structure. ACM Computing Surveys 1991; 23(3): 345–405.
- 33Peralta F, Reina DG, Toral S. Water quality online modeling using multi-objective and multi-agent Bayesian optimization with region partitioning. Mechatronics 2023; 91:102953.
- 34Cortes J, Martinez S, Karatas T, Bullo F. Coverage control for mobile sensing networks. IEEE Transactions on Robotics and Automation 2004; 20(2): 243–255.
- 35Xie J, Carrillo LRG, Jin L. An integrated traveling salesman and coverage path planning problem for unmanned aircraft systems. IEEE Control Systems Letters 2018; 3(1): 67–72.
10.1109/LCSYS.2018.2851661 Google Scholar
- 36Wu Y, Low KH, Lv C. Cooperative path planning for heterogeneous unmanned vehicles in a search-and-track mission aiming at an underwater target. IEEE Transactions on Vehicular Technology 2020; 69(6): 6782–6787.
- 37Zhao Q, Wang Y, Li Y. Voronoi tessellation-based regionalised segmentation for colour texture image. IET Computer Vision 2016; 10(7): 613–622.
- 38Peng F, Hu S, Gao Z, Zhou W, Sun H, Yu P. Chaotic particle swarm optimization algorithm with constraint handling and its application in combined bidding model. Computers and Electrical Engineering 2021; 95:107407.
- 39Tian D, Zhao X, Shi Z. Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization. Swarm and Evolutionary Computation 2019; 51:100573.
